Main
Again looking at acousticness, you’ll see a significant difference between Fruitvale station (romance) and Tenet (action). I used this chart to pick out certain tracks that will be analyzed on a deeper level later on.
For this research I am going to look at film composer and music producer Ludwig Göransson. I took data on nine of his latest film-scores from the Spotify API, and combined all the findings in this Correlalogram. Because his database is quite big, I needed to zoom in to only certain playlists and variables. I was curious beforehand if I could find some significant relations between different variables, to build my story on. As you can see the variable accousticness has two negative (significant) correlations. In the following pages I’m going to research what these changes mean and if I can show them in different ways.
So what actually is accousticness? It’s not that difficult, and quite self-explanatory. It means how accoustic a track is.
Again looking at acousticness, you’ll see a significant difference between Fruitvale station (romance) and Tenet (action). I used this chart to pick out certain tracks that will be analyzed on a deeper level later on.
As you can see Black Panther’s has some really interesting findings. For example, “Kilmonger challenge” has one of the highest loudness (as seen in the point size), and “Kilmonger” has one of the highest instrumentalness value. Another interesting finding is the song “Rainy night in Talin” from Tenet. This song has a really high acousticness, high energy and a BPM of 130.
Genres
chorddiagram
tone height/chroma/pitch class
chroma features
chromagram
logarithmic compression
portamento
transposition
Chromagram kilmonger
dynamic time warping(DTW)
basic approach feature space local cost measure local distance measure
cosine distance
warping path
compmus_normalise to normalise audio features using common techniques, including
compmus_normalise to normalise audio features using common techniques, including
compmus_align aligns two levels of structure with each other, e.g., Spotify segments with
compmus_summarise helps to summarise features within higher levels of structure, including:
We have seen that the principles of repetition, homogeneity, and novelty are fundamental for partitioning a given audio recording into musically meaningful structural elements. To study musical structures and their mutual relations, one general idea is to convert the music signal into a suitable feature sequence and then to compare each element of the feature sequence with all other elements of the sequence. This results in a self-similarity matrix(SSM), a tool which is of fundamental importance not only for music structure analysis but also for the analysis of many kinds of time series
For tempo i’ve chosen the song Kilmonger from the Black Panther’s score. I think this was an interesting song to analyse because of its different layers it contains. In the beginning you can hear a flute, which represents kilmonger’s African background. The flute is pitched down and escalates into chaos. The next layer are the strings and they go up in arpeggios. These escalate and grow bigger. Then suddenly the music cuts out and 808 drums start to kick. It feels dangerous and they come out of nowhere. They have a really low bass and sound like heartbeats. The last layer is the trap beat, representing him coming from Oakland.
’’ note / I want to show these layers with lines in the plots and be more specific about what happens with the changes in tempo.
***
Test
